# https://quantpedia.com/Screener/Details/53
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data import SubscriptionDataSource
from QuantConnect.Python import PythonData
from QuantConnect.Python import PythonQuandl
from datetime import date, timedelta, datetime
from decimal import Decimal
import numpy as np
from math import floor
import json
class SentimentAndStyleRotationAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetEndDate(2018, 7, 1)
self.SetCash(100000)
self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily)
self.AddData(CBOE, "PutCallRatio", Resolution.Daily)
self.vix_SMA_1 = SimpleMovingAverage(21)
self.vix_SMA_6 = SimpleMovingAverage(21*6)
self.PCRatio_SMA_1 = SimpleMovingAverage(21)
self.PCRatio_SMA_6 = SimpleMovingAverage(21*6)
# initialize the indicator with the history request
PCRatio_history = self.History(["PutCallRatio"], 21*10, Resolution.Daily)
vix_history = self.History(["CBOE/VIX"], 21*10, Resolution.Daily)
for tuple in vix_history.loc["CBOE/VIX"].itertuples():
self.vix_SMA_6.Update(tuple.Index, tuple.value)
self.vix_SMA_1.Update(tuple.Index, tuple.value)
for tuple in PCRatio_history.loc["PUTCALLRATIO"].itertuples():
self.PCRatio_SMA_1.Update(tuple.Index, tuple.value)
self.PCRatio_SMA_6.Update(tuple.Index, tuple.value)
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.AddEquity("SPY", Resolution.Daily)
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), self.rebalance)
self.month_start = False
self.selection = False
self.months = -1
def CoarseSelectionFunction(self, coarse):
if self.month_start:
# drop stocks which have no fundamental data or have low price
self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)]
return self.filtered_coarse
else:
return []
def FineSelectionFunction(self, fine):
if self.month_start:
self.selection = True
fine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0
and i.EarningReports.BasicEPS.TwelveMonths>0
and i.ValuationRatios.PERatio>0
and i.ValuationRatios.PBRatio>0]
# Calculate the market cap and add the "MakretCap" property to fine universe object
for i in fine:
#i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
sotrted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
decile_top1 = sotrted_market_cap[:floor(len(sotrted_market_cap)/10)]
decile_top2 = sotrted_market_cap[floor(len(sotrted_market_cap)/10):floor(len(sotrted_market_cap)*2/10)]
decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)]
sorted_PB1 = sorted(decile_top1, key = lambda x: x.ValuationRatios.PBRatio)
sorted_PB2 = sorted(decile_top2, key = lambda x: x.ValuationRatios.PBRatio)
sorted_PB3 = sorted(decile_top3, key = lambda x: x.ValuationRatios.PBRatio)
# The value portfolio consists of all firms included in the quintile with the lowest P/B ratio
PB_bottom1 = sorted_PB1[:floor(len(decile_top1)/5)]
PB_bottom2 = sorted_PB2[:floor(len(decile_top2)/5)]
PB_bottom3 = sorted_PB3[:floor(len(decile_top3)/5)]
self.value_portfolio = [i.Symbol for i in PB_bottom1 + PB_bottom2 + PB_bottom3]
# The growth portfolio consists of all firms included in the quintile with the highest P/B ratio
PB_top1 = sorted_PB1[-floor(len(decile_top1)/5):]
PB_top2 = sorted_PB2[-floor(len(decile_top2)/5):]
PB_top3 = sorted_PB3[-floor(len(decile_top3)/5):]
self.growth_portfolio = [i.Symbol for i in PB_top1 + PB_top2 + PB_top3]
return self.value_portfolio + self.growth_portfolio
else:
return []
def rebalance(self):
# rebalance every three months
self.months += 1
if self.months%3 == 0:
self.month_start = True
def OnData(self, data):
if self.Securities["CBOE/VIX"].Price == 0 or self.Securities["PutCallRatio"].Price == 0: return
self.vix_SMA_1.Update(self.Time, self.Securities["CBOE/VIX"].Price)
self.vix_SMA_6.Update(self.Time, self.Securities["CBOE/VIX"].Price)
self.PCRatio_SMA_1.Update(self.Time, self.Securities["PutCallRatio"].Price)
self.PCRatio_SMA_6.Update(self.Time, self.Securities["PutCallRatio"].Price)
if self.month_start and self.selection:
self.month_start = False
self.selection = False
stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for i in stocks_invested:
if i not in self.value_portfolio+self.growth_portfolio:
self.Liquidate(i)
if self.vix_SMA_1.Current.Value > self.vix_SMA_6.Current.Value:
if self.PCRatio_SMA_1.Current.Value < self.PCRatio_SMA_6.Current.Value:
long_weight = 1/len(self.value_portfolio)
for long in self.value_portfolio:
self.SetHoldings(long, long_weight)
elif self.PCRatio_SMA_1.Current.Value > self.PCRatio_SMA_6.Current.Value:
short_weight = 1/len(self.value_portfolio)
for short in self.value_portfolio:
self.SetHoldings(short, -short_weight)
else:
long_weight = 1/len(self.value_portfolio+self.growth_portfolio)
for long in self.value_portfolio+self.growth_portfolio:
self.SetHoldings(long, long_weight)
class QuandlVix(PythonQuandl):
def __init__(self):
self.ValueColumnName = "VIX Close"
class CBOE(PythonData):
'''Cboe Equity Volume Put/Call Ratios (11-01-2006 to present) Custom Data Class'''
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("https://cdn.cboe.com/resources/options/volume_and_call_put_ratios/equitypc.csv", SubscriptionTransportMedium.RemoteFile)
def Reader(self, config, line, date, isLiveMode):
if not (line.strip() and line[0].isdigit()): return None
index = CBOE()
index.Symbol = config.Symbol
try:
# Example File Format:
# DATE CALL PUT TOTAL P/C Ratio
# 11/1/06 976510 623929 1600439 0.64
data = line.split(',')
index.Time = datetime.strptime(data[0], "%m/%d/%Y").strftime("%Y-%m-%d")
index.Value = Decimal(data[4])
except ValueError:
return None
return index